Increasing the interpretability of complex models can help users make better use of algorithmic predictions [1]}.
Specifically, model explanations may help with debugging and detecting errors: if an algorithm is making decisions on the basis of irrelevant factors, this could be an indication that there is something wrong [2]}.
In a study of how organizations use explanation methods in practice, [3]} found that detecting errors is the most common use case for explanations.
However, researchers have struggled to demonstrate that explanations, which commonly focus on explaining model internals, lead to an improvement in decision quality [4]}.
